CN111506815A - Volunteer recommendation method before college entrance examination of full-time order - Google Patents
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Abstract
The invention belongs to the technical field of Internet big data mining and application, and discloses a method for recommending volunteers before admission of a vocational order, which specifically comprises the following steps: selecting an intent specialty; obtaining an examinee recommendation reference score; all intention orders are obtained according to professional screening of the intention; acquiring the lowest admission score of the intentions of each intention single-recruiting institution to the specialty, wherein the lowest admission score refers to the lowest admission score when the individual recruits in the previous year; calculating the admission probability of each intention single institution according to the recommended reference score and the lowest admission score; carrying out optimization recommendation on a plurality of intention single recruiters based on the sequence of the admission probability from high to low; in conclusion, based on the recommendation method provided by the invention, the examination room can be pertinently recommended to the examinees on the high-job single enrollment, the blindness problem of most examinees in the single enrollment volunteer report filling process is effectively solved, and the probability of the examinees being enrolled is effectively improved.
Description
Technical Field
The invention belongs to the technical field of Internet big data mining and application, and particularly relates to a method for recommending volunteers before enrollment of a high-job order.
Background
Along with the popularization and execution of the national high-post order enrollment policy, more and more examinees are added into an enrollment system for high-post order enrollment, and the high-post order enrollment is characterized in that: and carrying out volunteering before examination of the examinees, and then carrying out single-enrollment examination of the corresponding institutions according to the filled volunteers. Thus, there is a problem in the overall system: the number of the admission people and the number of the examination reporting people have larger uncertainty, and the examinees cannot reasonably and scientifically judge the individual admission schools, so that the examinees are blindly filled in the wish, and the admission probability of the examinees is lower.
In modern society, with the wide application of internet technology, various data are shared and are on the internet, forming an internet big data era, and data related to high-job postings are also included in internet data, so that related information of the high-job postings can be quickly known on the internet. The artificial intelligence algorithm is a deep learning method provided on the basis of the internet big data technology, and has the advantages of high calculation efficiency and high accuracy.
In conclusion, an artificial intelligence algorithm can be provided in a targeted manner on the basis of internet data, and volunteer recommendation is provided for examinees recruited by high-job orders.
Disclosure of Invention
In view of the above, the invention provides a method for recommending volunteers before full-time individual enrollment, which specifically utilizes historical data to calculate and analyze the individual enrollment probability, thereby providing scientific and reasonable volunteer enrollment suggestions for examinees, effectively solving the problem that the examinees are blindly enrolled when the volunteers are enrolled, and improving the enrollment probability of the examinees.
In order to achieve the purpose, the invention provides the following technical scheme: a volunteer recommending method before college entrance of a vocational order specifically comprises the following steps:
selecting an intent specialty;
obtaining an examinee recommendation reference score;
all intention orders are obtained according to professional screening of the intention;
acquiring the lowest admission score of the intentions of each intention single-recruiting institution to the specialty, wherein the lowest admission score refers to the lowest admission score when the individual recruits in the previous year;
calculating the admission probability of each intention single institution according to the recommended reference score and the lowest admission score;
and performing optimization recommendation of a plurality of intention single recruiters based on the sequence of the admission probability from high to low.
Preferably, the recommended reference achievements include written test achievements and other achievements.
Preferably, the other achievements are acquired in a user-defined preset mode. Specifically, the step of customizing and presetting the other achievements comprises the following steps: acquiring scoring principles and scoring intervals of other scores in the intention major; according to the scoring principle, other scores are preset in the scoring interval.
Preferably, the step of obtaining written test results comprises: acquiring at least 5 examinee examination results, and calculating the average value and mean square deviation of the at least 5 results; the difference between the mean and mean square error was calculated as the written test performance.
Preferably, the examination achievement is obtained through a simulated examination.
Preferably, before acquiring the test result, the method further comprises the following steps of identifying the number N of times of the test taker simulation test: when the number of times of examination N is less than 5, executing the simulation examination; when the number of times of examination is more than 5 and less than 10, obtaining 5 examination scores; when the number of times of examination is more than 10, obtaining the examination scores of N/2 times, wherein N/2 is a positive integer. Wherein the rounding principle of the N/2 is as follows: when N/2= M, N/2 is an integer M; when M is more than N/2 and more than M +1, N/2 is an integer M + 1.
Preferably, after identifying the number N of times of the test taker's simulated test, the method further comprises: identifying the time of each examinee simulation examination, and calculating the difference between the examination time and the current time; the examination scores are selected in turn according to the sequence of the time difference from small to large.
Preferably, before selecting an intent specialty, further comprising: obtaining a plurality of recruiting specialities in response to the recommendation request; acquiring the number of examination reports of each single enrollment specialty in the single enrollment of the previous year based on a plurality of single enrollment specialties; and optimally recommending a plurality of single enrollment specialties based on the principle that the number of the people who enter the examination is at least greater.
Compared with the prior art, the invention has the following beneficial effects:
based on the recommendation method provided by the invention, the examinees on the high-position single enrollment can be recommended to the examination institutions, the blindness problem of most examinees in the single enrollment volunteer reporting is effectively solved, and the probability of the examinees being enrolled is effectively improved.
Specifically, in the invention, the recommendation reference score of the examinee is obtained based on the simulated examination, the admission probability is calculated based on the recommendation reference score and the admission score of the single institution, and finally, the optimized recommendation is carried out according to the admission probability, so that the accuracy and pertinence of the recommendation are effectively improved.
In the above, the recommended reference score is formed by taking the written test score as a main score and other scores as auxiliary scores, and the written test score is calculated on the basis of at least 5 times of simulated test scores, so that the contingency of calculation and recommendation is effectively avoided.
Drawings
FIG. 1 is a simplified flow diagram of a recommendation method provided by the present invention;
fig. 2 is a detailed flowchart of the recommendation method provided in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for recommending volunteers before enrollment of a high-job order, in particular to a main recommendation flow of the recommendation method, please refer to fig. 1, which comprises the following main steps:
selecting an intent specialty;
obtaining an examinee recommendation reference score;
all intention orders are obtained according to professional screening of the intention;
acquiring the lowest admission score of the intentions of each intention single-recruiting institution to the specialty, wherein the lowest admission score refers to the lowest admission score when the individual recruits in the previous year;
calculating the admission probability of each intention single institution according to the recommended reference score and the lowest admission score;
and performing optimization recommendation of a plurality of intention single recruiters based on the sequence of the admission probability from high to low.
Please continue to refer to fig. 2, the main recommended flow is fully explained, which specifically includes the following steps:
s1, executing a simulation test to obtain the test score of an examinee;
specifically, in this step, the examination paper for the simulation examination mainly selects the true examination questions of the examination taken in the calendar year list.
S2, responding to a recommendation request, and acquiring a single professional recommendation;
specifically, in this step, the recommendation method for the single enrollment specialty is as follows:
s21, responding to the recommendation request to obtain a plurality of single recruitment specialties; specifically, the single admission specialties of a plurality of single admission specialties in the past year are subject to the standard, or the admission plan published by each province is subject to the standard.
S22, acquiring the number of examination reports of each single enrollment specialty in the previous year based on a plurality of single enrollment specialties.
And S23, carrying out optimization recommendation on a plurality of single enrollment specialties based on the principle that the number of the examination reporting persons is at least greater.
Under the condition of equal difference and the same recommendation probability, the number of the examination reporting persons corresponding to the a specialty is 100, and the single move in the previous year comprises the five a/b/c/d/e specialties which are easy to explain; b, the number of professional entrance examination reports is 110; c, the number of professional examination reports is 80; d, the number of professional examination reports is 200; the number of professional examination reporting persons is 50;
in summary, the list of finally obtained professional recommendation for single enrollment is: d/b/a/c/e.
S3, based on the optimization recommendation of the step S2, selecting an intention specialty from the single recruitment specialty recommendation list; specifically, the specialty a is taken as an example in the following.
And S4, self-defining and presetting other scores.
Specifically, in this step, the custom preset mode of other scores is as follows:
acquiring scoring principles and scoring intervals of other scores in the intention major;
according to the scoring principle, other scores are preset in the scoring interval.
It is easy to explain, take profession a as an example, set up the scoring interval of other achievements in profession a as 0-10 points, the principle of scoring is 0-6 points of failing, 6-7 points of passing, 7-9 points of moderate, 9-10 points of excellence; when the examinee presets by self, the examinee can set any score according to the ordinary learning state or examination state.
S5, obtaining the reference score recommended by the examinee;
specifically, in this step, the recommended reference achievement mainly includes the written test achievement and other achievements, wherein the other achievements are obtained according to the presetting in step S4, and the written test achievement is obtained based on the simulation examination in step S1.
The following principles should be satisfied in terms of acquiring written test results:
identifying the number N of times of the examinee simulation examination; specifically, when the number of tests N is less than 5, the simulation test described in step S1 is executed; when the number of times of examination is more than 5 and less than 10, obtaining 5 examination scores; when the number of times of examination is more than 10, obtaining the examination scores of N/2 times, wherein N/2 is a positive integer.
The rounding principle for N/2 is as follows: when N/2= M, N/2 is an integer M; when M is more than N/2 and more than M +1, N/2 is an integer M + 1. It is easy to explain, the number of times of the simulated examinations is set as 11, at this time, N/2=5.5, 5 < 5.5 < 6, and the corresponding N/2 is taken as an integer of 6, that is, 6 simulated examination results are taken when the reference result recommended by the examinee is obtained.
After 6 examination results are obtained, calculating the average value and mean square deviation of the 6 examination results;
the difference between the mean and mean square error was calculated as the written test performance. Easy to explain, suppose the average value of the examinee's 6 test results is 68 points in Chinese, 72 points in mathematics and 60 points in English; the mean square error is 5 points of Chinese monobasic mean square error, 5 points of mathematic monobasic mean square error, 5 points of English monobasic mean square error and 15 points of total mean square error; correspondingly, the final written test results should be in Chinese 63, mathematics 67, English 55, or total 185.
And S6, screening and obtaining all intentions according to the intentions. Specifically, assume that the monograph of the previous year includes a professional intention monograph institute A/B/C/D/E five.
S7, acquiring the lowest admission score of the intentions of each intention single-recruiting institution to the major, wherein the lowest admission score refers to the lowest admission score in the single recruiting of the previous year.
Easily explained, taking the written test result as an example, the lowest admission score of a major in the school of the A school in the single admission time of the previous year is 150 points; the lowest admission score of a specialty in school B is 160 points; the lowest admission score of a specialty in school C is 170 points; the lowest admission score of a specialty in school D is 180 points; the lowest admission score for specialty a at school E was 175.
S8, calculating the admission probability of each intention invitation institution according to the recommended reference score and the lowest admission score;
as an easily explained, as an implementation, the calculation method of the enrollment probability is:
the probability of enrollment being 1 mean square error above the minimum enrollment score for the intended specialty is 50%;
the admission probability of 1.5 mean square deviations above the minimum admission fraction for the intent specialty is 60%;
the admission probability of 2 mean square deviations above the minimum admission fraction for the intent specialty is 70%;
the admission probability of 2.5 mean square deviations above the minimum admission fraction for the intent specialty is 80%;
the probability of enrollment is 90% above the minimum enrollment score for the intended specialty by 3 and more mean square deviations.
In summary, the admission probability of institution A is 90%; the admission probability of institution B is 60%; the admission probability of academy C is 50%; the admission probability of institution D is less than 50%; the admission probability of institution E is less than 50%.
S9, carrying out optimization recommendation on a plurality of intention single recruiters based on the sequence of the admission probability from high to low;
specifically, in this step, no recommendation is made for institutions with a probability of admission below 50%, so that the final optimized recommendation list for specialty a is obtained as: colleges and universities A; colleges and universities B; colleges and universities C.
In addition, when the other results are failed, the admission probability of all institutions is lower than 50 percent in combination with other results; when other results are passed, the admission probability of all institutions is reduced by 10 percent; when other results are moderate, the admission probability of all universities is reduced by 5 percent; when other results are excellent, the admission probability of all institutions is not reduced. Therefore, after other scores are combined, the optimized recommendation sequence of each single institution is unchanged, but the admission probability is correspondingly changed, so as to provide further admission reference. Therefore, the overall recommendation method is mainly in the form of written test results and assists other results in the overall recommendation calculation, and other results are used as a reference value.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A method for recommending volunteers before enrollment of a full-time order is characterized by comprising the following steps:
selecting an intent specialty;
obtaining an examinee recommendation reference score;
all intention orders are obtained according to professional screening of the intention;
acquiring the lowest admission score of the intentions of each intention single-recruiting institution to the specialty, wherein the lowest admission score refers to the lowest admission score when the individual recruits in the previous year;
calculating the admission probability of each intention single institution according to the recommended reference score and the lowest admission score;
and performing optimization recommendation of a plurality of intention single recruiters based on the sequence of the admission probability from high to low.
2. The method of claim 1, wherein the method comprises: the recommended reference achievements include written test achievements and other achievements.
3. The method of claim 2, wherein the other achievements are obtained in a customized manner.
4. The method of claim 3, wherein the step of customizing the other achievement comprises:
acquiring scoring principles and scoring intervals of other scores in the intention major;
according to the scoring principle, other scores are preset in the scoring interval.
5. The method for pre-enrollment volunteer recommendation for an executive order according to any one of claims 2-4, wherein said step of obtaining written test results comprises:
acquiring at least 5 examinee examination results, and calculating the average value and mean square deviation of the at least 5 results;
the difference between the mean and mean square error was calculated as the written test performance.
6. The method of claim 5, wherein the test results are obtained through a simulated test.
7. The method of claim 6, further comprising identifying the number of test taker simulation exams N:
when the number of times of examination N is less than 5, executing the simulation examination;
when the number of times of examination is more than 5 and less than 10, obtaining 5 examination scores;
when the number of times of examination is more than 10, obtaining the examination scores of N/2 times, wherein N/2 is a positive integer.
8. The method as claimed in claim 7, wherein the rounding rule of N/2 is:
when N/2= M, N/2 is an integer M;
when M is more than N/2 and more than M +1, N/2 is an integer M + 1.
9. The method of claim 8, wherein after identifying the number N of test taker simulation exams, the method further comprises:
identifying the time of each examinee simulation examination, and calculating the difference between the examination time and the current time;
the examination scores are selected in turn according to the sequence of the time difference from small to large.
10. The method of claim 1, wherein prior to selecting an intent specialty, further comprising:
obtaining a plurality of recruiting specialities in response to the recommendation request;
acquiring the number of examination reports of each single enrollment specialty in the single enrollment of the previous year based on a plurality of single enrollment specialties;
and optimally recommending a plurality of single enrollment specialties based on the principle that the number of the people who enter the examination is at least greater.
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| CN112070376A (en) * | 2020-08-27 | 2020-12-11 | 北京国育未来文化发展有限公司 | College entrance examination volunteer recommendation method, device, terminal and computer readable storage medium |
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